Digests » 121
this week's favorite
We've applied reinforcement learning from human feedback to train language models that are better at summarization. Our models generate summaries that are better than summaries from 10x larger models trained only with supervised learning.
Today, we are happy to share our new advancements that not only push deep learning training to the extreme, but also democratize it for more people—from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU.
The problem regarding the use of machine learning in cyber security is difficult to solve because the advances in the field offer many opportunities that it is challenging to find exceptional and beneficial use cases for implementation and decision making. Moreover, such technologies can be used by intruders to attack computer systems. The goal of this list is to give you the tools and resources related to the use of machine learning for cyber security.
In this article, I will be implementing a Logistic Regression model without relying on Python’s easy-to-use sklearn library. This post aims to discuss the fundamental mathematics and statistics behind a Logistic Regression model.
Choosing good colors for your charts is hard. This article tries to make it easier. I want you to feel more confident in your color choices. And if you have no sense for colors at all, here’s my attempt to help you find good ones anyway. We’ll talk about common color mistakes I see out there in the wild, and how to avoid them.